The first part of the talk addresses the problem
of classifying image sets, each belonging to the same class and typically covering large appearance variations.
By modeling each image set as a manifold, the problem is formulated as the computation of the distance between
two manifolds, called as Manifold-Manifold Distance (MMD). Specifically, a manifold is expressed by a collection
of local linear models, each depicted by a subspace. MMD is then converted to integrating the distances between
pairs of subspaces respectively from one of the involved manifolds. We theoretically and experimentally compare
various configurations of the ingredients of MMD. The proposed method is applied to the task of face recognition
with image set (FRIS), where identification is achieved by seeking the minimum MMD between the gallery and probe
image sets. Our experiments demonstrate that, as a general set similarity measure, the proposed MMD consistently
outperforms other competing non-discriminative methods and is even promisingly comparable to the state-of-the-art
discriminant methods.

The second part of the talk focuses on learning-based face hallucination. Many
learning-based super-resolution methods are based on the manifold assumption, which claims that point-pairs from
the low-resolution representation manifold (LRM) and the corresponding high-resolution representation manifold
(HRM) possess similar local geometry. However, the manifold assumption does not hold well on the original coupled
manifolds (i.e., LRM and HRM) due to the nonisometric one-to-multiple mappings from low-resolution (LR) image
patches to high-resolution (HR) ones. To overcome this limitation, we propose a solution from the perspective of
manifold alignment. In this context, we perform alignment by learning two explicit mappings which project the
point-pairs from the original coupled manifolds into the embeddings of a common manifold (CM). For the task of SR
reconstruction, we treat HRM as target manifold and employ the manifold regularization to guarantee that the
local geometry of CM is more consistent with that of HRM than LRM is. After alignment, we carry out the SR
reconstruction based on neighbor embedding between the new couple of the CM and the target HRM. Experimental
results on face image super-resolution verify the effectiveness of our method.

Biography

Shiguang Shan
received the M.S. degree in computer science from the Harbin Institute of Technology, Harbin, China, in 1999, and
the Ph.D. degree in computer science from the Institute of Computing Technology (ICT), Chinese Academy of
Sciences (CAS), Beijing, in 2004. He has been with ICT, CAS since 2002 and has been an Associate Professor since
2005. He is also the Vice Director of the ICT–ISVISION Joint Research and Development Laboratory for Face
Recognition, ICT, CAS. His research interests cover image analysis, pattern recognition, and computer vision. He
is focusing especially on face recognition related research topics, and has published more than 120 papers on the
related research topics. Many of these papers are published in IEEE Transactions (e.g. T PAMI and T IP) or top-
level international conferences (e.g. CVPR and ICCV). He received the China’s State Scientific and Technological
Progress Awards in 2005 for his work on face recognition technologies. One of his papers in CVPR’08 won the “Best
Student Poster Award Runner-up”. He also won the Silver Medal of “Scopus Seeking Future Star of Science Award” in
2009. He is a member of the IEEE.